Design and Verification of a Small-Scale District Heating Network Experiment

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9101

M. Krieger, Dylan Stecklair, S. Peluso, S. Stockar

引用次数: 2

Abstract

The ability to combine energy sources with intermittent performance with more consistent form of power is crucial for increasing the penetration of renewable energy sources in the electricity and heat generation sector. In this scenario, district heating networks are a promising solution but, to benefit the most from this technology, control tools must be developed with the objective of optimizing the heating load to each of the buildings in the network, while rejecting external disturbances. One of the main challenges for control design and verification is the limited access to data and experimental platforms. In addition, real systems are subjected to a large number of exogenous inputs and tests repeatability for benchmarking is a challenge. To overcome this limitation, a scaled experimental set up has been developed. This paper discusses the design of the experimental setup of a simple heat distribution network as well as the derivation, calibration and validation of a simulation model. Simulation results show that the model error in predicting temperature is always below 1 %.

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小型区域供热网络实验设计与验证

将具有间歇性性能的能源与更一致的电力形式相结合的能力对于增加可再生能源在电力和热力发电部门的渗透至关重要。在这种情况下，区域供热网络是一个很有前途的解决方案，但是，为了从这项技术中获益最大，必须开发控制工具，以优化网络中每个建筑物的热负荷，同时拒绝外部干扰。控制设计和验证的主要挑战之一是对数据和实验平台的有限访问。此外，实际系统受到大量外源输入和测试的可重复性的基准是一个挑战。为了克服这一限制，研制了一种规模实验装置。本文讨论了简单配热网络实验装置的设计以及仿真模型的推导、标定和验证。仿真结果表明，该模型预测温度的误差始终在1%以下。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.